A Genetic Search in Policy Space for Solving Markov Decision Processes

Danny Barash

Markov Decision Processes (MDPs) have been studied extensively in the context of decision making under uncertainty. This paper presents a new methodology for solving MDPs, based on genetic algorithms. In particular, the importance of discounting in the new framework is dealt with and applied to a model problem. Comparison with the policy iteration algorithm from dynamic programming reveals the advantages and disadvantages of the proposed method.


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